budget-constrained-design
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npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/budget-constrained-designOptimize experiment design within compute and time budgets
- Solves the problem of designing experiments under limited resources
- Leverages factor identification, level specification, and design matrix construction
- Uses decision criteria based on available runs to select optimal design types
- Delivers a constrained design matrix that fits within budget limits
SKILL.md
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--- name: budget-constrained-design description: "Optimize experiment design under compute and time budget constraints" version: 1.0.0 category: experiment-execution type: tactic used-by: experiment-design orchestrates: - factor-identification - level-specification - design-matrix-construction --- # Tactic: Budget-Constrained Design ## Orchestration Pattern 1. **Assess Budget** → Determine available GPU-hours, wall-clock time, and cost ceiling 2. **factor-identification** → Identify all candidate factors 3. **Estimate Cost Per Run** → Calculate time/compute for a single experiment run 4. **Compute Maximum Runs** → budget / cost_per_run = max feasible runs 5. **level-specification** → Constrain levels to fit within run budget 6. **Select Design Type** → Choose most information-efficient design for the budget 7. **design-matrix-construction** → Build the constrained design matrix ## Decision Criteria | Available Runs | Recommended Approach | |---------------|---------------------| | < 10 | One-factor-at-a-time or Plackett-Burman screening | | 10-30 | Fractional factorial (Resolution III-IV) | | 30-60 | Fractional factorial (Resolution V) or Taguchi | | 60-120 | Full factorial on top factors + screening on rest | | 120+ | Full factorial or RSM with replication | ## Optimization Strategies - **Sequential Design**: Run screening first, then detailed study on important factors - **Adaptive Allocation**: Allocate more runs to high-variance conditions - **Early Stopping**: Define stopping criteria for clearly dominated configurations - **Transfer from Pilot**: Use pilot study results to inform main study design - **Shared Controls**: Reuse control/baseline runs across multiple comparisons ## Quality Checks - Does the design have sufficient power for the primary hypothesis? - Are the most important factors given priority in the allocation? - Is there a contingency plan if budget is cut mid-experiment? - Are early stopping criteria pre-defined (not post-hoc)? - Is the design balanced despite budget constraints?
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